商品识别篇：使用 ResNet 识别你的货架商品 扫码试看/订阅 《 TensorFlow 2项目进阶实战》视频课程 • 基础：图像分类问题定义与说明 • 基础：越来越深的图像分类网络 • 应⽤用：检测SKU抠图与分类标注流程 • 应⽤用：分类训练集与验证集划分 • 应⽤用：使⽤用TensorFlow 2训练ResNet • 应⽤用：使用ResNet识别货架商品 • 扩展：图像分类常用数据集综述 AlexNet（2012） VGGNet（2014） VGGNet（2014） GoogLeNet/Inception（2014） GoogLeNet/Inception（2014） ResNet（2015） ResNet（2015） 历年 SOTA 模型对比 应⽤用：检测 SKU 抠图与分类标注流程 … 检测框 -> SKU 小图 … SKU 小图 -> 手动分类 “Hello TensorFlow” 各品类数据统计 RP2K 样例数据（规格、细粒度、角度） “Hello TensorFlow” Try it！ 应⽤用：使⽤用 TensorFlow 2 训练 ResNet “Hello TensorFlow” Try it！ 应⽤用：使用ResNet识别货架商品 “Hello TensorFlow” Try it！ 扩展：图像分类常用数据集综述 https://github.com/zalan

the Aggregated Residual Transformations for Deep Neural Networks paper. It is based on the regular ResNet model, which substitutes 3x3 convolutions in the bottleneck block for 3x3 grouped convolutions. This the paper is in the backbone. Specifically, the VGG model is obsolete and has been replaced by the ResNet50 model. This model script is available on GitHub and NGC. ‣ Neural Collaborative Filtering (NCF) Filtering paper. This model script is available on GitHub and NGC. ‣ ResNet50 v1.5 model: This model is a modified version of the original ResNet50 v1 model. This model script is available on GitHub and NGC

.................................................... 88 16. Keras ― Real Time Prediction using ResNet Model ................................................................................. 89 17. the feature from ImageNet database. Some of the popular pre-trained models are listed below,  ResNet  VGG16  MobileNet  InceptionResNetV2  InceptionV3 Loading a model Keras pre-trained below: import keras import numpy as np from keras.applications import vgg16, inception_v3, resnet50, mobilenet #Load the VGG model vgg_model = vgg16.VGG16(weights='imagenet') #Load the Inception_V3

经典卷积网络 10.10 CIFAR10 与 VGG13 实战 10.11 卷积层变种 10.12 深度残差网络 10.13 DenseNet 10.14 CIFAR10 与 ResNet18 实战 10.15 参考文献 第 11 章 循环神经网络 11.1 序列表示方法 11.2 循环神经网络 11.3 梯度传播 11.4 RNN 层使用方法 11 名在 Top-5 错误率上降低了惊人的 10.9%。 自 AlexNet 模型提出后，各种各样的算法模型相继被发表，其中有 VGG 系列、 GoogLeNet 系列、ResNet 系列、DenseNet 系列等。ResNet 系列模型将网络的层数提升至数 百层、甚至上千层，同时保持性能不变甚至更优。它算法思想简单，具有普适性，并且效 果显著，是深度学习最具代表性的模型。 除了有监督学习领域 2009 2012 AlexNet 提出 GAN生成 对抗网络 2014 2015 DQN AlphaGO 2016 2017 AlphaGO Zero 2019 OpenAI Five ResNet 2015 2014 VGG GooLeNet 2015 Batch Normalization 德州扑克 Pluribus 2019 机器翻译 BERT 2018 TensorFlow

The SimCLR fine-tuned checkpoint with ResNet-50 as the encoder architecture also achieved a better accuracy on ImageNet with only 10% labels, when compared to a ResNet-50 that was trained from scratch with hyperparameters. For example, the paper titled: ResNet Strikes Back by Wightman et al.14 demonstrates improvement in the accuracy achieved by the ResNet-50 model from 75.2% to 80.4% without using any additional additional data or a teacher model. Even though ResNet-50 was introduced back in 2015, updating it with newer learning techniques improved the accuracy significantly without having to change anything in

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 274 7.6 残差网络（ResNet） . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 275 7.6.1 函数类 . 残差块 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 277 7.6.3 ResNet模型 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 279 7.6.4 训练模型 稠密连接网络（DenseNet） . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283 7.7.1 从ResNet到DenseNet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283 7.7.2 稠密块体 . .

in the UK (hence, the name). Instead of training a model from scratch, we will use a pre-trained ResNet50 model (trained on the imagenet dataset). Next, we will finetune (retrain) it with the flower dataset the validation sets. It is a small sample to train a good quality model. So, we use a pre-trained ResNet50 model and fine tune it. The code for this project is available as a Jupyter notebook here. Tensorflow use a pre-trained ResNet50 model with the top (softmax) layer replaced with a new softmax layer with 102 units (one unit for each class). Additionally, we add the recommended resnet preprocessing layer

SSD_MobileNet1.0 SSD_ResNet50 Yolov3 w/o w/ Amazon DeepLens Speedup 0 1 2 3 SSD_MobileNet1.0 SSD_ResNet50 Yolov3 w/o w/ Acer aiSage Speedup 0 0.5 1 1.5 2 2.5 SSD_MobileNet1.0 SSD_ResNet50 Yolov3 SSD_MobileNet1.0 SSD_ResNet50 Yolov3 w/o w/ Amazon DeepLens Speedup 0 5 10 15 SSD_MobileNet1.0 SSD_ResNet50 Yolov3 w/o w/ Acer aiSage Speedup 0 10 20 30 40 SSD_MobileNet1.0 SSD_ResNet50 Yolov3 w/o

13.2 图像分类模型的示例代码 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158 13.2.1 使用 ResNet50 进行 ImageNet 分类 . . . . . . . . . . . . . . . . . . . . . . 158 13.2.2 使用 VGG16 提取特征 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164 目录 IX 13.3.4 ResNet50 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165 13.3.5 InceptionV3 如何在 Keras 中使用预训练的模型？ 我们提供了以下图像分类模型的代码和预训练的权重： • Xception • VGG16 • VGG19 • ResNet50 • Inception v3 • Inception-ResNet v2 • MobileNet v1 它们可以使用 keras.applications 模块进行导入： from keras.applications